Active Mapping of Underwater Litter Using Camera-Sonar Fusion

Abstract:Marine litter is a growing threat to the underwater ecosystem, driving demand for autonomous survey methods that can locate debris efficiently over large areas. Existing survey methods typically follow predefined paths or operate with a single sensing modality, typically a camera (with image quality suffering in poor-visibility conditions) or sonar (usually noisy and low-resolution). We present an active mapping framework in which a forward-looking sonar and a camera both feed into a shared Bayesian occupancy map, and an optimization problem is solved at each step to decide on the next best view. Candidate viewpoints are scored by a two-term utility that balances exploration of uncertain regions via voxel entropy against exploitation of likely objects. Each sensor is characterized by range- and bearing-dependent detection and false-alarm probability tables determined from data. We evaluate the approach in a realistic underwater simulator, demonstrating that active mapping finds objects faster than a lawnmower coverage pattern, and that the dual-sensor approach works better than using either of the individual sensors.

Robotics (cs.RO)

arXiv:2609.39898 [cs.RO]

(or arXiv:2609.39898v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.39898 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1109/AQTR70159.2026.11577897 Focus to learn more DOI(s) linking to related resources

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